Bibliographic record
Abstract
Despite a critical shortage of virgin aggregate, the availability of demolished concrete for use as recycled concrete aggregate (RCA) is increasing. Using this waste concrete as RCA conserves virgin aggregate, reduces the impact on landfills, decreases energy consumption, and can provide cost savings. However, there are still many unanswered questions about the beneficial use of RCA in concrete pavements. This research studied the effect of RCA on the coefficient of thermal expansion (CTE) and its impact on pavement performance. CTE is a key property of concrete and relates to the amount of expansion and contraction caused by changes in temperature. CTE testing was conducted on 16 cores containing various amounts of coarse RCA (0%, 15%, 30%, and 50%) using a simplified methodology. Testing showed that concrete performance improved as the amount of RCA increased. This result was demonstrated by a decrease in CTE; values for the CTE ranged from 7.28 × 10 -6 /°C for 0% coarse RCA to 4.10 × 10 -6 /°C for 50% coarse RCA. The variability of the CTE results was also examined to assess whether the RCA content or simplified testing methodology affected the results. Performance of the RCA concrete was simulated by using the Mechanistic–Empirical Pavement Design Guide. Average, minimum, and maximum CTE values for each RCA amount were used to investigate the sensitivity of this important property on pavement roughness, cracking, and faulting. Simulated pavement performance of all the RCA sections improved as the CTE values decreased.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".